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- W2251894552 abstract "We present Plato, a probabilistic model for entity resolution that includes a novel approach for handling noisy or uninformative features, and supplements labeled training data derived from Wikipedia with a very large unlabeled text corpus. Training and inference in the proposed model can easily be distributed across many servers, allowing it to scale to over 10 7 entities. We evaluate Plato on three standard datasets for entity resolution. Our approach achieves the best results to-date on TAC KBP 2011 and is highly competitive on both the CoNLL 2003 and TAC KBP 2012 datasets." @default.
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- W2251894552 date "2015-12-01" @default.
- W2251894552 modified "2023-09-25" @default.
- W2251894552 title "Plato: A Selective Context Model for Entity Resolution" @default.
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- W2251894552 doi "https://doi.org/10.1162/tacl_a_00154" @default.
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